Machine-Learning-Based Water Quality Monitoring

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (22 April 2024) | Viewed by 1821

Special Issue Editors


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Guest Editor
Computer Science and Engineering Department, Faculty of Automatic Control and Computers, National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania
Interests: software engineering; control engineering; IoT; mobile technologies; machine learning; serious gaming; crowdsensing; system integration; water distribution systems

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Guest Editor
Department of Computer Science, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
Interests: software engineering; distributed computing; data mining; skills and expertise; environmental analysis; system integration; environmental management systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Computer Science and Engineering Department, Faculty of Automatic Control and Computers, National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania
Interests: big data analytics; time series analysis; data science; machine learning; deep learning; natural language processing; text mining; data mining; cloud computing; high-performance computing

Special Issue Information

Dear Colleagues,

Since the dawn of civilization, societies have developed technologies to improve daily life. Today, information technology represents a driving force in societal evolution, fostering accelerated progress and innovation. High-quality water resources are vital to society and rely on emerging technologies and information systems for improving the quality of service and monitoring solutions. Nonetheless, the pursuit of smart water initiatives must harmoniously prioritize both societal well-being and environmental sustainability for developing intelligent environments.

This Special Issue gathers research papers, reviews, and contributions from experts at the intersection of machine learning and environmental science and aims to address the following key inquiries:

  • To what extent can water quality management be facilitated by machine learning techniques?
  • How can intelligent monitoring solutions be integrated in the context of water infrastructure?
  • How can machine learning support high-quality, proactive, and personalized services for humans?

We are pleased to invite you to contribute original research based on machine learning techniques and their applications focused on our most vital resource—water. The primary goal is to provide a comprehensive overview of recent advancements, methodologies, and case studies related to the utilization of machine learning for monitoring and managing water quality, including, but not limited to, the following:

  • Machine learning and deep learning solutions for smart water quality monitoring;
  • The integration of machine learning within smart water infrastructure;
  • Decision support and data-driven solutions for water quality management.

Dr. Alexandru Predescu
Prof. Dr. Mariana Mocanu
Dr. Elena Simona Apostol
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart water solutions
  • water quality monitoring
  • sustainable water resources
  • machine learning techniques
  • deep learning techniques
  • decision support systems
  • water resource management
  • data science
  • information technology
  • computational methods

Published Papers (2 papers)

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Research

11 pages, 6155 KiB  
Article
An Image Analysis of River-Floating Waste Materials by Using Deep Learning Techniques
by Maiyatat Nunkhaw and Hitoshi Miyamoto
Water 2024, 16(10), 1373; https://doi.org/10.3390/w16101373 - 11 May 2024
Viewed by 464
Abstract
Plastic pollution in the ocean is a severe environmental problem worldwide because rivers carry plastic waste from human activities, harming the ocean’s health, ecosystems, and people. Therefore, monitoring the amount of plastic waste flowing from rivers and streams worldwide is crucial. In response [...] Read more.
Plastic pollution in the ocean is a severe environmental problem worldwide because rivers carry plastic waste from human activities, harming the ocean’s health, ecosystems, and people. Therefore, monitoring the amount of plastic waste flowing from rivers and streams worldwide is crucial. In response to this issue of river-floating waste, our present research aimed to develop an automated waste measurement method tailored for real rivers. To achieve this, we considered three scenarios: clear visibility, partially submerged waste, and collective mass. We proposed the use of object detection and tracking techniques based on deep learning architectures, specifically the You Only Look Once (YOLOv5) and Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT). The types of waste classified in this research included cans, cartons, plastic bottles, foams, glasses, papers, and plastics in laboratory flume experiments. Our results demonstrated that the refined YOLOv5, when applied to river-floating waste images, achieved high classification accuracy, with 88% or more for the mean average precision. The floating waste tracking using DeepSORT also attained F1 scores high enough for accurate waste counting. Furthermore, we evaluated the proposed method across the three different scenarios, each achieving an 80% accuracy rate, suggesting its potential applicability in real river environments. These results strongly support the effectiveness of our proposed method, leveraging the two deep learning architectures for detecting and tracking river-floating waste with high accuracy. Full article
(This article belongs to the Special Issue Machine-Learning-Based Water Quality Monitoring)
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11 pages, 1038 KiB  
Article
Identifying the Most Discriminative Parameter for Water Quality Prediction Using Machine Learning Algorithms
by Tapan Chatterjee, Usha Rani Gogoi, Animesh Samanta, Ayan Chatterjee, Mritunjay Kumar Singh and Srinivas Pasupuleti
Water 2024, 16(3), 481; https://doi.org/10.3390/w16030481 - 1 Feb 2024
Cited by 1 | Viewed by 1063
Abstract
Groundwater quality is one of the major concerns. Quality of the groundwater directly impacts human health, growth of plants and vegetables. Due to the severe impacts of inadequate water quality, it is imperative to find a swift and economical solution. Water quality prediction [...] Read more.
Groundwater quality is one of the major concerns. Quality of the groundwater directly impacts human health, growth of plants and vegetables. Due to the severe impacts of inadequate water quality, it is imperative to find a swift and economical solution. Water quality prediction may help us to manage water resources properly. The present study has been carried out considering thirty-seven water sample data points form the Pindrawan tank command area of Raipur district, Chhattisgarh, India. A total of nineteen physicochemical parameters were measured, out of which seventeen parameters were used to compute the weight-based groundwater quality index (WQI). In this present work, the primary goal is to identify the most effective parameters for WQI prediction. Out of the seventeen parameters tested, the Mann—Whitney—Wilcoxon (MWW) statistical test has revealed that five parameters Fe, Cr, Na, Ca, and Mg hold a strong statistical significance in distinguishing between drinkable and non-drinkable water. Out of these five parameters, Cr is the only parameter that maintains a different range of values for drinkable water and non-drinkable water. To validate the efficiency of these statistically significant parameters, machine learning techniques like Artificial Neural Networks (ANN) and Logistic Regression (LR) were used. The experimental results clearly demonstrate that out of all the seventeen parameters tested, utilizing only Cr yields remarkably high classification accuracy. ‘Cr’ achieved an accuracy of 91.67% using artificial neural networks. This is much higher than the accuracy of 66.67% obtained using a parameter set with all seventeen parameters. The proposed methodology achieved good accuracy when classifying water samples into drinkable and non-drinkable water using only one parameter, ‘Cr’. Full article
(This article belongs to the Special Issue Machine-Learning-Based Water Quality Monitoring)
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